Abstract
While the recent technological advancements have enabled instructors to deliver mathematical concepts and theories beyond the physical boundaries innovatively and interactively, poor performance and low success rate in mathematic courses have always been a major concern of educators. More specifically, in an online learning environment, where students are not physically present in the classroom and access course materials over the network, it is toilsome for course coordinators to track and monitor every student’s academic learning and experiences. Thus, automated student performance monitoring is indispensable since it is easy for online students, especially those underperforming, to be “out of sight,” hence getting derailed and off-track. Since student learning and performance are evolving over time, it is reasonable to consider student performance monitoring as a time-series problem and implement a time-series predictive model to forecast students’ educational progress and achievement. This research paper presents a case study from a higher education institute where interaction data and course achievement of a previously offered online course are used to develop a time-series predictive model using a Long Short-Term Memory network, a special kind of Recurrent Neural Network architecture. The proposed model makes predictions of student status at any given time of the semester by examining the trend or pattern learned in the previous events. The model reported an average classification accuracy of 86 and 84% with the training dataset and testing dataset, respectively. The proposed model is trialed on selected online math courses with exciting yet dissimilar trends recorded.
Highlights
Students are the main stakeholders in any educational institute [1, 2] and their academic excellence determines the extent to which they have achieved their academic goals [3,4,5]
This research was conducted at the the University of the South Pacific (USP), which is a multi-modal, multi-cultural and multicampus higher education institution comprising of a vibrant and culturally diverse community of staff and students from its 12 member countries: Cook Islands, Fiji, Kiribati, Marshall Islands, Nauru, Niue, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu and Vanuatu, spreads across 33 million square kilometres of ocean
Since its establishment in 1968, USP has produced more than 50,000 graduates and has awarded more than 70,000 qualifications [40], empowering Pacific Islanders with knowledge and skills in various disciplines and enabling them to contribute to lead cohesive, resilient and sustainable communities in the region
Summary
While educational institutes are responsible for providing a conducive learning environment and effective support services for their students, they are invariably accountable for their students’ performance. The educators are made responsible for ensuring every student’s achievement and success and providing additional support to students at risk of academic failure [6, 7]. Educators and, more recently, the administrators of educational institutes continuously monitor and track students’ performance to evaluate the effectiveness of their teaching and learning practices and add timely support interventions for students to succeed. Effective student progress and performance monitoring assist institutions and educators in determining students’ proficiency. Academic progress and achievement monitoring is a critical component of modern education and student support services
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